Match S/W to App

Screen shot #13 (Merely a picture to illustrate that our GUI is totally self-explanatory)

TK-MIPs underlying Software Architecture can be reconfigured to match the structure of your application.

The flowchart above provides insights into how TK-MIP may best be reconfigured for most efficient processing to accommodate specific application structural simplifications. For situations involving time stamps used in data logging, let the above defined delta = t_(j+1) - t_(j).

The best discussion of the proper sequence of discrete-time KF operations (to be implemented into software) that I have encountered to date is found on pages 234-236 of
Brown, Robert Grover, Hwang, Patrick Y. C., Introduction to Random Signals and Applied Kalman Filtering, 2nd Edition, John Wiley & Sons, Inc., New York, 1983, 1992.
They also discuss an alternate formulation for implementing a discrete-time KF into software in Section 6.2 on pages 259-261 that is also correct but looks a little different. [They also provide an excellent derivation and discussion of D. T. Magills Interactive Multiple Model (IMM) bank-of-Kalman-Filters approach to adaptive filtering (also called the Multiple Model Estimation Algorithm (MMEA) in Section 9.3 of the above cited book.]

Kalman Filter Structure for handling the automatic processing of two different periodic measurement streams (of different periods) from different types of measurement structures and quality (demonstrated here using MatLab).

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